How to Build a Self-Improving AI Agent

TL;DR

A self-improving agent stores its successes and failures, retrieves relevant past experiences before acting, and adapts its behavior based on what worked. This requires persistent trace storage with semantic search - exactly what Deeplake and Hivemind provide. The agent loop becomes: act, evaluate, store the outcome, retrieve relevant history next time.

Overview

Self-improvement means the agent gets better at its job over time without retraining the base model. The key insight is that improvement comes from experience data: what the agent tried, what worked, what failed, and why. If you store this data in a searchable format and retrieve it at decision time, the agent naturally improves - it has access to its own track record.

The Self-Improvement Loop

┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐
│   Act    │────▶│ Evaluate │────▶│  Store   │────▶│ Retrieve │
│          │     │ outcome  │     │ in memory│     │ next time│
└──────────┘     └──────────┘     └──────────┘     └────┬─────┘
      ▲                                                  │
      └──────────────────────────────────────────────────┘

Implementation with Deeplake

1. Experience Store

python

import deeplake
 
experiences = deeplake.open("al://my-org/agent-experiences")
 
experiences.add_column("task_description", deeplake.types.Text())
experiences.add_column("task_embedding", deeplake.types.Embedding(1536))
experiences.add_column("approach", deeplake.types.Text())
experiences.add_column("outcome", deeplake.types.Text())  # "success", "failure", "partial"
experiences.add_column("score", deeplake.types.Float32())
experiences.add_column("lessons", deeplake.types.Text())
experiences.add_column("metadata", deeplake.types.Json())
experiences.add_column("timestamp", deeplake.types.Int64())

2. Before Acting: Retrieve Past Experience

python

def get_relevant_experience(task: str, top_k: int = 5):
    """Find similar past tasks and what worked."""
    results = experiences.query("""
        SELECT task_description, approach, outcome, score, lessons
        FROM agent_experiences
        ORDER BY cosine_similarity(task_embedding, :q)
        LIMIT :k
    """, {"q": embed(task), "k": top_k})
    
    # Format as context for the LLM
    context = "Past experience with similar tasks:\n"
    for r in results:
        context += f"- Task: {r['task_description']}\n"
        context += f"  Approach: {r['approach']}\n"
        context += f"  Outcome: {r['outcome']} (score: {r['score']})\n"
        context += f"  Lesson: {r['lessons']}\n\n"
    return context

3. After Acting: Evaluate and Store

python

def evaluate_and_store(task: str, approach: str, result: dict):
    """Evaluate the outcome and persist the experience."""
    evaluation = llm.evaluate(task, approach, result)
    
    experiences.append({
        "task_description": task,
        "task_embedding": embed(task),
        "approach": approach,
        "outcome": evaluation["outcome"],
        "score": evaluation["score"],
        "lessons": evaluation["lessons"],
        "metadata": {"tokens_used": result["tokens"], "duration": result["duration"]},
        "timestamp": int(time.time())
    })

Why Hivemind Accelerates Self-Improvement

With Hivemind, self-improvement happens across your entire team of agents:

Shared experience pool: One agent's success teaches all agents
Cross-session learning: Experiences persist forever, searchable by similarity
Automatic trace logging: Every agent action is stored without custom code
Team-wide patterns: Surface organization-wide best practices from agent behavior

Citations

Hivemind: shared memory for agent teams

Install Hivemind